Neural Networks

Molecular transistors as open fermionic quantum systems

Etchegoin, P. (2002) · Physica A Statistical Mechanics and its Applications · doi:10.1016/s0378-4371(02)00996-2 · Cited by 2

ONN the Use of Neural Networks for Data Privacy

Pont-Tuset, J., Medrano−Gracia, P., Nin, J., et al. (2008) · Springer eBooks · doi:10.1007/978-3-540-77566-9_55 · Cited by 2

Morphogenetic and Evolutionary Approach to Similar Image Creation

Ohnishi, K., & Mario, K. (2014) · Advances in intelligent systems and computing · doi:10.1007/978-3-319-10783-7_36 · Cited by 2

Feature Learning Architecture Taxonomy and Neuroscience Background

Krig, S. (2016) · Springer eBooks · doi:10.1007/978-3-319-33762-3_9 · Cited by 2

Adaptive Virtual Topology Control Based on Attractor Selection

Koizumi, Y., Arakawa, S., & Murata, M. (2017) · Modeling and optimization in science and technologies · doi:10.1007/978-3-319-50920-4_12 · Cited by 2

Real-Time Gaze Control Using Neurofuzzy Prediction System

Díaz-Cortés, M.-A., Cuevas, E., & Rojas, R. (2017) · Intelligent systems reference library · doi:10.1007/978-3-319-57813-2_7 · Cited by 2

A Perceptron Classifier, Its Correctness Proof and a Probabilistic Interpretation

Falkowski, B.-J. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-59650-1_21 · Cited by 2

Modulation of Central Pattern Generators (CPG) for the Locomotion Planning of an Articulated Robot

Mesa, E. M. R., & Hernández-Riveros, J.-A. (2018) · Communications in computer and information science · doi:10.1007/978-3-030-01535-0_24 · Cited by 2

VERIFICATION OF UNRELIABLE PARAMETERS OF THE MALICIOUS INFORMATION DETECTION MODEL

Kotenko, I. V., & Parashchuk, I. B. (2019) · VESTNIK OF ASTRAKHAN STATE TECHNICAL UNIVERSITY SERIES MANAGEMENT COMPUTER SCIENCE AND INFORMATICS · doi:10.24143/2072-9502-2019-2-7-18 · Cited by 2

Selection of Countermeasures against Harmful Information based on the Assessment of Semantic Content of Information Objects in the Conditions of Uncertainty

Parashchuk, I. B., Doynikova, E. V., Saenko, I. B., et al. (2020) · International Conference on Innovations in Intelligent Systems and Applications (INISTA) · doi:10.1109/inista49547.2020.9194680 · Cited by 2

No Regular Behavior Pattern in Neural Network Execution – A Matlab Experience

Ashek-Al-Aziz, M., Muntakim, A.-H., & Ahmed, M. K. (2021) · International Journal of Computer Applications · doi:10.5120/ijca2021921087 · Cited by 2

Introduction

Herrera, F., Charte, F., Rivera, A. J., & del Jesús, M. J. (2016) · Springer eBooks · doi:10.1007/978-3-319-41111-8_1 · Cited by 3

Multivariate Datenanalyse

Schneider, M. (2020) · Springer eBooks · doi:10.1007/978-3-662-61866-0_6 · Cited by 2

Towards New Generation, Biologically Plausible Deep Neural Network Learning

Apparaju, A., & Arandjelović, O. (2022) · Sci · doi:10.3390/sci4040046 · Cited by 3

Biomimetics Analyzed: Examples from an Epistemological and Ontological Perspective

Drack, M., & Jansen, L. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-39504-8_19 · Cited by 3

On-NAS: On-Device Neural Architecture Search on Memory-Constrained Intelligent Embedded Systems

Kim, B., & Lee, S. (2023) · ACM Conference on Embedded Networked Sensor Systems (SenSys) · doi:10.1145/3625687.3625814 · Cited by 2

A Multiobjective Evolutionary Optimized Recurrent Neural Network for Defects Detection on Flat Panel Displays

Abeysundara, H. A., Hamori, H., Matsui, T., et al. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-12054-6_15 · Cited by 2

Genetic Optimization of Type-2 Fuzzy Integrators in Ensembles of ANFIS Models for Time Series Prediction

Soto, J., & Melín, P. (2014) · Studies in computational intelligence · doi:10.1007/978-3-319-05170-3_6 · Cited by 2

Identifying Indistinguishable Classes in Multi-class Classification Data Sets Using ELM

Dwiyasa, F., & Lim, M. (2014) · Proceedings in adaptation, learning and optimization · doi:10.1007/978-3-319-14063-6_34 · Cited by 2

Using Radial Basis Function Networks for Hand Gesture Recognition

Salomon, R., & Weissmann, J. (2001) · Studies in fuzziness and soft computing · doi:10.1007/978-3-7908-1826-0_2 · Cited by 2

The Square Circle

Angere, S. (2017) · Metaphilosophy · doi:10.1111/meta.12224 · Cited by 2

A Novel Osmosis-Inspired Algorithm for Multiobjective Optimization

Rotar, C., Iantovics, L. B., & Arik, S. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-70093-9_9 · Cited by 2

Study on hot rolling process of the D51 large-diameter threaded bars

Kim, J.-M., Won, S., & Kim, B. (2018) · Journal of Mechanical Science and Technology · doi:10.1007/s12206-018-0332-4 · Cited by 2

Prediction in Nature-Inspired Dynamic Optimization

Meier, A., & Krämer, O. (2020) · Springer tracts in nature-inspired computing · doi:10.1007/978-981-15-2133-1_2 · Cited by 3

Using Self-Organizing Maps for Intelligent Camera-Based User Interfaces

Banković, Z., Romero, E., Blesa, J., et al. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-13803-4_60 · Cited by 2

The Paramountcy of Reconfigurable Computing

Hartenstein, R. W. (2012) · doi:10.1002/9781118342015.ch18 · Cited by 3

The impact of pre-clustering on classification of heterogeneous protein data

Altartouri, H., Tamimi, H., & Ashhab, Y. (2021) · Network Modeling Analysis in Health Informatics and Bioinformatics · doi:10.1007/s13721-021-00336-0 · Cited by 1

Multimedia Content Analysis

Friedland, G. H., & Jain, R. (2014) · Cambridge University Press eBooks · doi:10.1017/cbo9781139049351.018 · Cited by 1

Tropical Cyclone Intensity Prediction Using BP-RNN From GPS-Derived Precipitable Water Vapor and Surface Meteorological Data

Chabibi, F. F., Putri, N., & Wijaya, D. D. (2024) · IOP Conference Series Earth and Environmental Science · doi:10.1088/1755-1315/1418/1/012020 · Cited by 1

Machine Learning-Based Surrogate Modeling for Enhancing the Magnet Design of Future Tokamaks

Giannini, L., & Boso, D. P. (2025) · IEEE Transactions on Applied Superconductivity · doi:10.1109/tasc.2025.3550890 · Cited by 1

Generalization-based similarity

Antić, C. (2025) · Annals of Mathematics and Artificial Intelligence · doi:10.1007/s10472-025-09989-3 · Cited by 5

Application of VGG-16 and modified VGG-16 for railway fault classification using image datasets

Kaddour, G., Abdelmalek, B., Yazid, L. D. L., et al. (2025) · International Conference on Electronics, Energy and Measurement (IC2EM) · doi:10.1109/ic2em63689.2025.11101246 · Cited by 1

Gradient neural network for time-varying matrix inversion with momentum acceleration and applications in image encryption

Shirilord, A., Dehghan, M., & Stanimirović, P. S. (2026) · Computational and Applied Mathematics · doi:10.1007/s40314-026-03777-3 · Cited by 1

EMORBFN: An Evolutionary Multiobjetive Optimization Algorithm for RBFN Design

López, P. L., Rivera, A. J., Pérez-Godoy, M. D., et al. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-02478-8_94 · Cited by 1

Domains of Competence of Artificial Neural Networks Using Measures of Separability of Classes

Luengo, J., & Herrera, F. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-02478-8_11 · Cited by 1

Fault Diagnosis of Condenser in Ship Steam Power System Based on Unsupervised Learning Neural Network

Yu, R., Xiang, R., Ke, Z. W., et al. (2012) · Applied Mechanics and Materials · doi:10.4028/www.scientific.net/amm.271-272.1568 · Cited by 1

Modeling of Osmotic Pressure of Aqueous Poly(Ethylene Glycol) Solutions Using the Artificial Neural Network and Free Volume Flory Huggins Model

Naeini, A. T., Pazuki, G. R., Vossoughi, M., et al. (2011) · Journal of Dispersion Science and Technology · doi:10.1080/01932691.2010.488137 · Cited by 1

Learning Landmark Selection Policies for Mapping Unknown Environments

Strasdat, H., Stachniss, C., & Burgard, W. (2011) · Springer tracts in advanced robotics · doi:10.1007/978-3-642-19457-3_29 · Cited by 1

Your skin knows when you will jump

Laufer, L., & Németh, B. (2007) · doi:10.1145/1362550.1362608 · Cited by 1

Creativity or mental illness: Possible errors of relational priming in neural networks of the brain

Swain, J. E., & Swain, J. D. (2008) · Behavioral and Brain Sciences · doi:10.1017/s0140525x08004688 · Cited by 1

Temperature rise estimation of substation connectors using data-driven models: Case: Thermal conveccion response

Giacometto, F., Capelli, F., Sala, E., Riba, J., & Romeral, L. (2015) · Annual Conference of the IEEE Industrial Electronics Society (IECON) · doi:10.1109/iecon.2015.7392717 · Cited by 1

A Nonlinear Neural Network’s Stability Analysis and Its kWTA Application

Yan, Y. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-25393-0_47 · Cited by 1

Multilevel and Color Image Segmentation by NSGA II Based OptiMUSIG Activation Function

De, S., Bhattacharyya, S., & Chakraborty, S. (2015) · Advances in computational intelligence and robotics book series · doi:10.4018/978-1-4666-9474-3.ch011 · Cited by 1

16. Detection of Buried Steel Drums from Magnetic Anomaly Data Using an Artificial Intelligence Technique

Salem, A., Ravat, D., Gamey, T. J., & Ushijima, K. (2005) · Society of Exploration Geophysicists eBooks · doi:10.1190/1.9781560801719.ch16 · Cited by 1

Die Grundlagen des E-Business

Kollmann, T. (2019) · Springer eBooks · doi:10.1007/978-3-658-26143-6_1 · Cited by 1

Harnessing the swarm: technological applications of collective intelligence

Sadedin, S., & Duéñez‐Guzmán, E. A. (2011) · Cambridge University Press eBooks · doi:10.1017/cbo9780511980381.020 · Cited by 1

Neural Network Control Strategies Applied to a DC Motor with a Nonlinear Load

Ferreira, L. M. M. L., & Barbosa, R. S. (2016) · Lecture notes in electrical engineering · doi:10.1007/978-3-319-43671-5_20 · Cited by 1

Kapacitásmodellezés és az IT-architektúratervezés kihívásainak vizsgálata statisztikai és prediktív analitikai eszközökkel

Fehér, P., Kő, A., & Szabó, Z. (2016) · Statisztikai Szemle · doi:10.20311/stat2016.11-12.hu1149 · Cited by 1

Network Science as New Systemics

Minati, G., & Pessa, E. (2018) · Contemporary systems thinking · doi:10.1007/978-1-4939-7581-5_8 · Cited by 1

Maximum 2 satisfiability logical rule in restrictive learning environment

Kasihmuddin, M. S. M., Mansor, M. A., & Sathasivam, S. (2018) · AIP conference proceedings · doi:10.1063/1.5041552 · Cited by 1

Sąsūkos dirbtinių neuronų tinklų įgyvendinimas spartinančiuosiuose įrenginiuose vaizdams analizuoti realiuoju laiku

Šabanovič, E. (2019) · doi:10.20334/2019-051-m · Cited by 1

Exactly Predictable Functions for Simple Neural Networks

Yost, J., Rizo, L., Fang, X., Su, Q. C., & Grobe, R. (2021) · SN Computer Science · doi:10.1007/s42979-021-00949-2 · Cited by 1

Introduction

Bhattacharyya, S., & Maulik, U. (2013) · Springer eBooks · doi:10.1007/978-3-642-40255-5_1 · Cited by 1

Einleitung

Keller, H. B. (2019) · Springer eBooks · doi:10.1007/978-3-658-26641-7_1 · Cited by 1

Using Machine Learning and Probabilistic Frameworks to Enhance Incident and Problem Management

Sailer, A., Mahindru, R., Song, Y., et al. (2015) · Advances in logistics, operations, and management science book series · doi:10.4018/978-1-4666-8496-6.ch010 · Cited by 1

Software implementation of the system for learning to write Chinese characters

Gubanov, N. V. (2023) · Herald of Dagestan State Technical University Technical Sciences · doi:10.21822/2073-6185-2023-50-2-58-66 · Cited by 1

Mapas predictivos con redes neuronales a partir de propiedades físicas de las rocas: caso de estudio en la Mesa de Los Santos (Santander)

García-Arias, S., Patiño, F. A. V., Gómez, J. D. S., & Ardila, M. T. U. (2024) · Revista EIA · doi:10.24050/reia.v21i41.1731 · Cited by 2

Effort Estimation in Agile Software Development Using Autoencoders

Sánchez, E. R., Santacruz, E. F. V., & Maceda, H. C. (2023) · International Conference on Software Process Improvement (CIMPS) · doi:10.1109/cimps61323.2023.10528839 · Cited by 1

Uniform approximation of a class of impulsive delayed Hopfield neural networks on the half-line

Hartung, F., Pinto, M., & Torres, R. (2024) · Electronic journal of qualitative theory of differential equations · doi:10.14232/ejqtde.2024.1.63 · Cited by 1

Analysis and modeling of travel behaviors of public transit passengers in car-oriented developing countries

Mujalli, R. O., Marie, I., Haddad, M., et al. (2025) · Innovative Infrastructure Solutions · doi:10.1007/s41062-025-01875-5 · Cited by 1

Untapped Potential in Self-Optimization of Hopfield Networks: The Creativity of Unsupervised Learning

Weber, N., Guckelsberger, C., & Froese, T. (2025) · Artificial Life · doi:10.1162/artl.a.10 · Cited by 2

Prediction of response of RC multi-span beams through a proposed ANN–FEA model

Ahmad, A., Kotsovos, G. M., Cotsovos, D. M., et al. (2025) · Proceedings of the Institution of Civil Engineers - Structures and Buildings · doi:10.1680/jstbu.24.00179 · Cited by 1

A Hybrid Method Combining Modular Neural Networks with Fuzzy Integration for Human Identification Based on Hand Geometric Information

Sánchez, J. L., & Melín, P. (2014) · Studies in computational intelligence · doi:10.1007/978-3-319-05170-3_28 · Cited by 1

A Fast Approximate Sparse Coding Networks and Application to Image Denoising

Cui, J., Qi, J., & Li, D. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-39065-4_74 · Cited by 1

Multilayer Perceptrons as Classifiers Guided by Mutual Information and Trained with Genetic Algorithms

Neme, A., Hernández, S., Nido, A., et al. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-32639-4_22 · Cited by 1

Prediction of seawall foundation settlement based on the improved variable dimension fraction and artificial neural network model

Qin, P., & Zhi-hai, Q. (2010) · International Conference on Advanced Computer Control · doi:10.1109/icacc.2010.5486909 · Cited by 1

A new oscillating-error technique for classifiers

Greer, K. (2017) · Cogent Engineering · doi:10.1080/23311916.2017.1293480 · Cited by 1

Solving inversion problem for refractivity estimation using Artificial Neural Networks

Tepecik, C., & Navruz, İ. (2015) · doi:10.1109/eleco.2015.7394523 · Cited by 1

Decision-Making Bias in Instance Matching Model Selection

Kejriwal, M., & Miranker, D. P. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-25007-6_23 · Cited by 1

Pojav tehnološke triade: opisni pojem današnje totalne realnosti

Mičunović, M., Badurina, B., & Bosančić, B. (2016) · Knjižnica revija za področje bibliotekarstva in informacijske znanosti · doi:10.55741/knj.60.1.13880 · Cited by 1

Supervised Learning Techniques for Body Mass Estimation in Bioarchaeology

Ionescu, V.-S., Czibula, G., & Teletin, M. (2017) · Advances in intelligent systems and computing · doi:10.1007/978-3-319-62524-9_7 · Cited by 1

Cardiac Arrhythmia Detection Using Computational Intelligence Techniques Based on ECG Signals

Lima, J. C. C., de Lima, A. F., de Souza, D. A., et al. (2020) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-45691-7_48 · Cited by 1

Dynamics of a Recurrent Neural Network with Impulsive Effects and Piecewise Constant Argument

Akhmet, M. U., Çinçin, D. A., & Cengiz, N. (2021) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-3-030-79357-9_52 · Cited by 1

Effizienzsteigerung durch datenbasierte Modellierung von Qualität und Produktionskostenfaktoren in der Vliesstoffindustrie

Cloppenburg, F. (2021) · Springer eBooks · doi:10.1007/978-3-662-62915-4_17 · Cited by 1

Создание математической модели нейронной сети для морфологической оценки репарации и ремоделирования костного дефекта

Fedosova, N. V., Берченко, Г. Н., Gn, B., & Mashoshin, D. V. (2021) · Matematicheskoe modelirovanie · doi:10.20948/mm-2021-09-02 · Cited by 1

Quantum Operator Model for Data Analysis and Forecast

Danko, G. L. (2021) · Applied Mathematics · doi:10.4236/am.2021.1211064 · Cited by 1

Inszenieren und Mobilisieren: Rechte und islamistische Akteure digital und analog

Birsl, U., Junk, J., Kahl, M., Pelzer, R., & Budrich, V. B. (2021) · Verlag Barbara Budrich eBooks · doi:10.3224/84742488 · Cited by 1

Facial Recognition Application with Hyperparameter Optimisation

Claus, H. M., Grab, C., Woroszyllo, P., & Rybarczyk, P. (2022) · Studies in computational intelligence · doi:10.1007/978-3-030-87954-9_6 · Cited by 1

Global and Regional Features

Krig, S. (2016) · Springer eBooks · doi:10.1007/978-3-319-33762-3_3 · Cited by 1

Translation of Medical Texts using Neural Networks

Wołk, K., & Marasek, K. P. (2019) · IGI Global eBooks · doi:10.4018/978-1-7998-0414-7.ch063 · Cited by 1

Neural Networks

Ben‐Ari, M., & Mondada, F. (2017) · Springer eBooks · doi:10.1007/978-3-319-62533-1_13 · Cited by 1

Multiobjective Metamodel-Based Design Optimization—A Review and Classification Approach Using the Example of Engine Development

Held, S., Hildenbrand, A., Herdt, A., et al. (2023) · SAE technical papers on CD-ROM/SAE technical paper series · doi:10.4271/2023-01-5026 · Cited by 1

Discovering User Interests by Document Classification

Nguyen, L. (2010) · Studies in computational intelligence · doi:10.1007/978-3-642-13422-7_9 · Cited by 1

Vision Pipelines and Optimizations

Krig, S. (2016) · Springer eBooks · doi:10.1007/978-3-319-33762-3_8 · Cited by 1

Non-exact complexity reduction of generalized neuro-fuzzy networks

Takács, O., & Várkonyi-Kóczy, A. R. (2005) · Proceedings of ... IEEE International Conference on Fuzzy Systems · doi:10.1109/fuzz.2001.1009123 · Cited by 1

Identification and Characterization of Microorganisms by Vibrational Spectroscopy

Stöckel, S., Walter, A., Boßecker, A., et al. (2012) · doi:10.1002/9783527643981.bphot012 · Cited by 1

Adaptive learning of fuzzy BSB and GBSB neural models

Bodyanskiy, Y. V., & Тесленко, Н. А. (2006) · Cybernetics and Systems Analysis · doi:10.1007/s10559-006-0119-y · Cited by 1

Retinal Processing: From Biology to Models and Applications

Alleysson, D., & Guyader, N. (2015) · Wiley eBooks · doi:10.1002/9783527680863.ch3 · Cited by 1

Agentenbasierte Simulation von Aktienmärkten

Ehrentreich, N. (2004) · Deutscher Universitätsverlag eBooks · doi:10.1007/978-3-322-81191-2_5 · Cited by 1

Indirect On‐Line Tool Wear Monitoring

Sick, B. (2004) · doi:10.1002/9780471683407.ch6 · Cited by 1

Texture Analysis

Haidekker, M. A. (2010) · doi:10.1002/9780470872093.ch8 · Cited by 1

Measure–Correlate–Predict

Zhang, M. H. (2015) · doi:10.1002/9781118900116.ch6 · Cited by 1

Different Kinds of Effect Estimators

Parker, A., Simari, G. I., Sliva, A. L., et al. (2013) · SpringerBriefs in computer science · doi:10.1007/978-1-4939-0274-3_3

From Configurable Circuits to Bio-Inspired Systems

Sipper, M., Sánchez, E. G., Haenni, J.-O., et al. (2000) · International series in intelligent technologies · doi:10.1007/978-1-4615-4401-2_4

Image/Video Compression with Artificial Neural Networks

Palacio, D. Z., Crespo, R. A. G., Fernández, G. G., et al. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-02481-8_47

Application of Soft Computing Techniques to Classification of Licensed Subjects

Kubalı́k, J., Jiřina, M., Starý, O., et al. (2006) · Kluwer Academic Publishers eBooks · doi:10.1007/0-387-22829-2_52

Towards Risk Minimization for Novice Gamblers: A ‘Not So Expert’ System

McGrath, G. M. (2009) · Springer eBooks · doi:10.1007/978-3-211-93971-0_11

Using Self-Organizing Maps to Control Physical Robots with Omnidirectional Drives

Salomon, R., Burchardt, H., & Schulz, T. (2006) · IEEE International Symposium on Circuits and Systems proceedings · doi:10.1109/iscas.2006.1693560

Comparative analysis of different learning algorithms on a common neural network structure

Moraga, C., & Gasch, A. (2003) · doi:10.1109/sccc.1999.810149

Quick Energy Drop in Stochastic 2D Minority

Regnault, D. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-79992-4_39

Susquehanna Chorale Spring Concert "Roots and Wings"

Hawkins, D. (2017)

Using DataGrid Control to Realize DataBase of Querying in VB6.0

Hu, S. (2000) · Modern Computer

Study and Two Types of Typical Usage of DataGrid Web Server Control

Hai-tao, L. (2005) · Modern Computer

Achieving Parameter of DBSCAN Based on Datagrid

Xu, Q. (2010)

PACWON: A parallelizing compiler for workstations on a network

Das, D. K., & Das, P. P. (1998) · Journal of Systems Architecture · doi:10.1016/s1383-7621(97)00078-7